Image quality assessment involves the assessment of the quality of images that are intended for human consumption. Image quality is a characteristic of an image that relates to perceived image degradation as compared to an ideal or perfect image reference (e.g., a target or un-degraded image reference). Images such as those that are provided by photographs can go through many stages of processing that affect image quality before they are presented to a human observer.
For example, each stage of image processing can introduce distortion that can reduce the quality of the final image that is produced. Images acquired by cameras can exhibit distortions due to optics, sensor noise, color calibration, exposure control, camera motion, etc. Other sources of distortion can include compression algorithms and bit errors.
Conventional image quality assessment systems attempt to assess the quality of images and to gauge the distortion that is added to an image during various stages of processing. Moreover, these systems attempt to automatically assess perceived image quality using quantitative measures. Image quality assessment metrics can play an important role in applications, such as image acquisition, compression, communication, displaying, printing, restoration, enhancement, analysis and watermarking.
In the signal and image processing fields the most common measures for judging image quality are PSNR (Peak-Signal-To-Noise) quality measures. However, it is well known that PSNR quality measures do not correlate well with perceptual quality. Furthermore, such measures can require a reference image for comparison, making them useful only in limited situations, such as synthetic experiments. Unfortunately, there are numerous cases where a reference image may be unavailable, such as situations that involve the judging of the quality of a de-noising algorithm on a real-world dataset, where the underlying noise-free image is unknowable. In this case, one would need to employ a “no-reference” or “blind” measure to render a quality assessment. Indeed, in most practical cases, a reference image is not available, and consequently in such cases image quality assessment is made more difficult.
Accordingly, a challenge that confronts conventional image quality assessment systems is to provide assessments when neither the reference image nor the image distortion type is known. One conventional approach uses distortion specific image quality measures as well as a distortion type classifier as tools for image quality assessment. However, conventional approaches, including this one, exhibit bias across distortion types. In addition, conventional approaches have proved to be limited in their capacity to provide image quality assessments that reflect perceptual quality. Accordingly, these approaches are not always useful for assessing quality as perceived by human observers.